CN113706009A

Movatterモバイル変換

Info

Publication number: CN113706009A
Application number: CN202110989019.3A
Authority: CN
Inventors: 朱昱成
Original assignee: Sichuan Cric Technology Co ltd
Current assignee: Sichuan Cric Technology Co ltd
Priority date: 2021-08-26
Filing date: 2021-08-26
Publication date: 2021-11-26

Abstract

Translated fromChinese

本发明提出—种基于多维度物料数据的供应商智能评价和分级系统，其特征在于，包括基础信息维护模块、评价计算内核模块、评价结果输出模块、关键数据项展示模块和用户与权限模块；本发明集成MES、WMS、SRM、ERP等多个业务系统数据和企业信息爬虫数据，实现高频数据采集和数据汇聚，大大降低了供应商评价数据源的获取门槛和获取难度；从多个维度对供应商进行评价，涵盖多项关键评价指标；使用以AHP层次分析法为核心的动态权重调整模型，满足不同背景下的供应商评价需要；各类指标自动进行分档计分与标准化处理，对实时评价和评价结果进行实时输出，对供应商行为进行多方位的评价监督、以及管理。

The present invention proposes a supplier intelligence evaluation and grading system based on multi-dimensional material data, which is characterized in that it includes a basic information maintenance module, an evaluation calculation kernel module, an evaluation result output module, a key data item display module, and a user and authority module; The invention integrates MES, WMS, SRM, ERP and other business system data and enterprise information crawler data, realizes high-frequency data collection and data aggregation, and greatly reduces the acquisition threshold and difficulty of supplier evaluation data sources; The evaluation of suppliers covers a number of key evaluation indicators; the dynamic weight adjustment model centered on the AHP analytic hierarchy process is used to meet the needs of supplier evaluation under different backgrounds; various indicators are automatically scored and standardized. Real-time output of real-time evaluation and evaluation results, and multi-dimensional evaluation, supervision and management of supplier behavior.

Description

Supplier intelligent evaluation and grading system based on multi-dimensional material data

Technical Field

The invention relates to the technical field of supply chain management, in particular to a multi-dimensional material data-based supplier intelligent evaluation and grading optimization system in a purchasing link of the manufacturing industry.

Background

At present, global economy integration makes manufacturing enterprises' competition increasingly fierce, and manufacturing enterprises pursue management and control internal management in the past, along with the competition intensification, the thinking of enterprise is also constantly expanding and extending, improves the requirement to the supplier, screens out the supplier that is rigorous and accords with the regulation, becomes the necessary mode that makes the enterprise keep the competitive advantage. Enterprises need to have over-hard capacity and cooperation of other enterprises when producing a product, however, different suppliers have differences in product quality, delivery time and the like, the performance of the same supplier on part of indexes is not stable enough, and purchasing enterprises often lack scientific and objective evaluation systems and data-driven evaluation processes. The reasonable evaluation of the suppliers is an ideal method for solving the problem, and a selection method and an evaluation mechanism of the suppliers are discussed and formulated, so that a high-quality management mechanism is determined, and an enterprise can effectively audit and manage the suppliers.

At present, supplier evaluation of manufacturing enterprises often adopts an artificial offline statistical calculation mode, and the following problems generally exist in the process and the result:

(1) supplier evaluation data is wide in source, multiple departments need to cooperate, and establishment of a richer and more complete evaluation system often leads to higher difficulty in obtaining original data and lower evaluation efficiency of the supplier;

(2) part of assessment indexes of a supplier, particularly indexes related to a production process, need a large amount of data as supports, are high in statistical difficulty, often need to send special persons for daily summary statistics, and are extremely high in workload;

(3) in the process of artificial statistics and calculation, the difficulty of partial index statistics and calculation is high, and the situations of data omission, calculation errors and the like often exist;

(4) and the evaluation result is calculated by using Excel offline, and the calculation process is not intuitive.

On-line system for supplier evaluation also has the following problems:

(1) the supplier evaluation system lacks integration with other systems, does not have a path for data exchange of an evaluation data source and an evaluation result, and forms a data island;

(2) the data sources are many, the structure difference is large, and the system is poor in data acquisition and preprocessing.

Disclosure of Invention

In order to solve the technical problems, the application provides a supplier intelligent evaluation and grading optimization system based on multi-dimensional material data, the technologies such as cloud computing and crawler are adopted, data in all links from purchasing to production and manufacturing are collected and used, supplier evaluation results are output, and effective support for grading optimization of suppliers is achieved.

The technical scheme adopted by the invention for solving the problems is as follows:

a supplier intelligent evaluation and grading system based on multi-dimensional material data is characterized by comprising a basic information maintenance module, an evaluation calculation kernel module, an evaluation result output module, a key data item display module and a user and authority module;

the system comprises a basic information maintenance module, a service management module and a service management module, wherein the basic information maintenance module is used for integrating a plurality of service system data and crawler data acquired by a crawler and providing a manual maintenance inlet of supplier evaluation source data;

the evaluation calculation kernel comprises three submodules, namely an evaluation system for evaluating three-layer indexes, a dynamic weight model taking an AHP analytic hierarchy process as a core and a calculation rule;

the evaluation result output module is used for outputting the supplier evaluation result based on the full-flow data and the dynamic weight model and is divided into an evaluation grade catalog and a supplier evaluation detail billboard;

the key data item display module is used for displaying the supplier evaluation and the total condition or key data of the supplier evaluation system;

the user and authority module comprises a user login submodule and a user management submodule, wherein the user login submodule is used for authenticating the identity of a user in the login process, and distributing different authorities to users with different identities, so that the users with different identities enter respective interfaces to obtain the interface presentation required by the user; and the user management submodule is used for a system administrator to check and modify the user information of the current system.

The further technical scheme is as follows: the construction process of the dynamic weight model comprises the following steps: establishing a hierarchical structure model according to an evaluation system of an evaluation calculation kernel, and establishing a judgment matrix of the importance degree of each index corresponding to a superior index; calculating a feature vector and carrying out normalization processing to obtain a weight vector; and carrying out consistency check on the weight calculation result.

The further technical scheme is as follows: the setting method of the calculation rule comprises the following steps:

according to the original data of the basic information maintenance module, data screening is carried out in a counting and summing mode according to the name of a supplier, the name of a material or other attributes, and the preprocessing of the original data is completed; and calculating to obtain a final evaluation result according to the calculation mode corresponding to each evaluation index and the known index weight.

The further technical scheme is as follows: the evaluation index implements three sets of evaluation rules:

grading and scoring: calculating indexes such as production yield and the like by adopting a PPM quality system, namely a defect statistical method of parts per million, and presetting a PPM range and a corresponding score;

counting and scoring: recording the occurrence frequency of the index, and multiplying the occurrence frequency by a scoring coefficient to obtain a final numerical value of the index;

thirdly, standardized scoring: counting according to the relative value of the total amount of the supplied quantity, or standardizing the index value, converting the index value into a standard value without dimension and magnitude difference, and standardizing the index values of different suppliers and different materials of the same material by (original value-mean value)/standard difference to obtain the index value.

The further technical scheme is as follows: the evaluation grade catalog at least comprises data visualization charts of all evaluation results, key index values, evaluation grade ratios and trends of suppliers and provides self-service analysis functions of screening, sorting and exporting; the supplier evaluation detail billboard displays basic information, subdivision index evaluation results, evaluation calculation processes, traceability data, purchase records, material information, historical evaluation, evaluation scores, evaluation grades and similar suppliers of a certain company.

The further technical scheme is as follows: the display mode and the display content of the key data item display module are specifically as follows: adding key data items of the number of suppliers, the material number, the average score and the evaluation completion times on a system home page for displaying, and simultaneously displaying the average score and the variation condition of the ring ratio of the number of unqualified suppliers in the month; the number of the suppliers is the total number of the suppliers participating in the evaluation of the suppliers in the current month; the number of the materials is the total number of the materials involved in the evaluation of the supplier in the current month; the average score is the arithmetic mean of the final evaluation scores of all suppliers participating in the evaluation of the suppliers in the month; the evaluation completion times are the accumulated times for completing the evaluation of the supplier in all time, and comprise monthly evaluation completion times, semiannual evaluation completion times and annual evaluation completion times.

The invention has the beneficial effects that:

compared with the conventional evaluation method, the system has the following characteristics:

(1) the supplier evaluation system integrates multiple service system data such as MES and WMS and enterprise information crawler data, realizes the real-time collection of the key indexes of the supplier, and provides powerful data support for the evaluation of the supplier;

(2) by using a dynamic weight adjustment model taking an AHP analytic hierarchy process as a core, various indexes are automatically graded, scored and standardized, and the evaluation requirements of suppliers under special backgrounds of highly difficult production materials, new product development, secondary materials, auxiliary materials and the like are met;

(3) the method and the device realize real-time evaluation of the suppliers and real-time output of evaluation results, help enterprises to conveniently and efficiently evaluate the suppliers, and obtain scientific and reasonable evaluation results.

Drawings

FIG. 1 is a schematic diagram of a three-level index evaluation system constructed according to an embodiment of the present invention;

FIG. 2 is a diagram of a preferred system architecture for supplier intelligent evaluation and ranking based on multidimensional material data in accordance with an embodiment of the present invention.

Detailed Description

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are only a few embodiments of the present invention, and not all embodiments of the present invention, and that the present invention is not limited by the embodiments described herein.

Examples

The invention discloses a multi-dimensional material data-based supplier intelligent evaluation and grading system, which comprises a basic information maintenance module, an evaluation calculation kernel module, an evaluation result output module, a key data item display module and a user and authority module, wherein the basic information maintenance module is used for storing basic information;

and the basic information maintenance module is used for efficiently integrating a plurality of service system data such as MES, WMS and the like and crawler data such as enterprise basic industrial and commercial information, lawsuit information and the like which are acquired by a crawler at regular time, and providing a manual maintenance entrance of supplier evaluation source data. In consideration of data accuracy, the system is different from other WEB systems in that manual maintenance entries are reserved for automatically acquired data, and a user can switch between an automatic mode and a manual mode as required.

The evaluation calculation kernel comprises three submodules: an evaluation system, a dynamic weight model and a calculation rule. The construction process of the three sub-modules is specifically as follows:

firstly, an evaluation system comprising three index evaluations is constructed by following the evaluation principle of 'quantitative as the main and qualitative as the auxiliary' and taking 'comprehensive, concrete and objective' as the target,

then, the system adopts a dynamic weight model taking an AHP (advanced high performance analysis) analytic hierarchy process as a core to meet the evaluation requirements of suppliers under different backgrounds, the AHP analytic hierarchy process combines the qualitative and quantitative methods, can be used for multi-target decision analysis, is proposed in the last 70 th century by Saaty of American operational scientists, and is suitable for application scenes with complicated target structures and lack of necessary data by quantifying the empirical judgment of decision makers. The indexes on the same layer are compared pairwise to construct a judgment matrix, and the weight of the lower layer to the indexes on the upper layer is calculated by using a certain mathematical processing method such as column item normalization processing, so that the method is an analysis method integrating the hierarchy, the weight and the decision. The method mainly comprises the steps of establishing a hierarchical structure model according to an evaluation system of an evaluation calculation kernel, constructing a judgment matrix of the importance degree of each index corresponding to a superior index, calculating a characteristic vector, carrying out normalization processing to obtain a weight vector, and carrying out consistency check on weight calculation results. The model can realize the following effects: the dynamically constructed calculation formula is used for replacing the original invariable calculation formula, so that different suppliers and different materials can be endowed with different weights of various indexes in the calculation formula according to the characteristics and properties of the suppliers and the different materials, a more accurate calculation result is obtained, and the problem that the part with new product development and high production difficulty is low in score is solved.

Finally, according to the service data acquired from the external system, the crawler data and the original data imported by the user, data screening is carried out in a counting and summing mode according to the name of a supplier, the name of a material or other attributes, and the preprocessing of the original data is completed; and calculating to obtain a final evaluation result according to the calculation mode corresponding to each evaluation index and the known index weight. The evaluation index implements three sets of evaluation rules:

And the evaluation result output module is used for displaying the supplier evaluation and the overall situation or key data of the supplier evaluation system. And outputting a supplier evaluation result based on the full-process data and the dynamic weight evaluation model, and dividing the supplier evaluation result into an evaluation grade catalog and a supplier evaluation detail billboard. The evaluation grade catalog comprises visual list display of all evaluation results and key index values of suppliers and data visualization charts of evaluation grade proportion, trend and the like of the suppliers, and provides self-service analysis functions of screening, sorting, exporting and the like. The supplier evaluation detail billboard displays basic information, subdivision index evaluation results, evaluation calculation processes, traceability data, purchase records, material information, historical evaluation, evaluation scores, evaluation grades, similar suppliers and the like of a certain company in detail.

And the key data item display module is used for displaying the supplier evaluation and the total condition or the key data of the supplier evaluation system. And adding the display of key data items such as the number of suppliers, the material number, the average score, the evaluation completion times and the like on the system home page. The number of the suppliers is the total number of the suppliers participating in the evaluation of the suppliers in the current month; the number of the materials is the total number of the materials related to the evaluation of the supplier in the current month; the average score is the arithmetic average of the final evaluation scores of all suppliers participating in the evaluation of the suppliers in the month, and the evaluation completion times are the accumulated times of completing the evaluation of the suppliers in all the time and comprise monthly evaluation completion times, semiannual evaluation completion times and annual evaluation completion times. And meanwhile, the average score and the variation condition of the ring ratio of the number of unqualified suppliers in the month are displayed.

The user and authority module, the user template contains user login submodule and user management submodule. The user login submodule is used for authenticating the identity of a user in the login process, and distributing different authorities to users with different identities, so that the users with different identities enter respective interfaces to obtain the interface presentation required by the users; and the user management submodule is used for a system administrator to check and modify the user information of the current system.

As shown in fig. 1-2, the detailed workflow of the supplier intelligent evaluation and grading optimization system based on multidimensional material data is as follows:

s100, acquiring basic data, wherein the basic data comprises an external system, a crawler data set and three types of source data manually maintained and imported by a user;

s200, establishing a three-level index evaluation system, and establishing the three-level index evaluation system according to the purchasing and production characteristics of the manufacturing enterprises, wherein the first-level index is as follows: evaluating daily goods supply performance, checking a provider on site and providing basic information of the provider; the second-level index is refined aiming at the first-level index; the third-level index is the further refinement of the second-level index.

Taking a first-level index of daily goods supply performance evaluation as an example, the method mainly comprises three aspects of quality, delivery and service, and carries out all-round evaluation on a provider;

the quality assessment of the suppliers mainly comprises seven secondary indexes of the qualification rate of the incoming inspection batch, the production offline rate, customer complaints, the occurrence number of 8D reports, the occurrence number of selection, the occurrence number of batch quality abnormity and rationalization suggestions: wherein, the three indexes of the qualification rate of the incoming inspection batch, the production offline rate and the rationalization suggestion are the scoring indexes; customer complaints, 8D report occurrence number, selection occurrence number and batch quality abnormity occurrence number are used as mark deduction indexes; and respectively carrying out statistical calculation on the process offline index and the non-process offline index.

The delivery evaluation mainly comprises three secondary indexes of supply timeliness, delivery quantity and delivery complaint: the timely supply means that a supplier delivers corresponding batches of goods on time, which are items added; the delivery quantity is the specific percentage of the defect number and is a deduction item; the delivery complaints refer to the marked contents of error, incompleteness, damage, pollution, unclear handwriting, etc., which are the items of deduction.

The service of the supplier mainly comprises three secondary indexes of communication timeliness, emergency delivery and technical support: the communication timeliness refers to whether an 8D report, letter, notice, drawing and the like which require to be replied reply within a specified time limit or not; the emergency delivery refers to the emergency arrival times within 24 hours, and is specifically determined by a buyer according to the emergency arrival time and importance; the technical support refers to the times of giving technical support in projects such as product development and quality improvement, and is specifically determined by a supplier management and review group. The three secondary indexes are all addendum items.

Step S300, constructing a dynamic weight model with an AHP analytic hierarchy process as a core

The specific mode comprises the following four steps:

establishing a hierarchical structure model: and (3) primarily establishing a hierarchical structure model of a supplier evaluation index system, and starting from a general target, further dividing the model into a first-level index layer, a second-level index layer and a third-level index layer. Taking the performance evaluation of the primary index supplier as an example, as shown in table 1;

TABLE 1 materials supply commercial performance evaluation index system

As shown in table 1, the performance evaluation index system model for daily supply of goods and materials supplier is divided into a target layer, a first-level index layer, a second-level index layer and a third-level index layer.

Secondly, constructing a judgment matrix:

judging the structure of the matrix needs to determine the importance degree of each index, assigning values by referring to expert opinions and related empirical data, and scaling each index by using a 1-9 scaling method, which is specifically shown in table 2.

TABLE 2 judge matrix Scale and its meanings

And respectively scoring the importance degrees of the target layer, the primary index, the secondary index and the tertiary index corresponding to the importance degrees of the superior index, and then carrying out comprehensive analysis to obtain a judgment matrix. Taking daily performance evaluation of the first-level index as an example, taking the table 3 as a first-level index judgment matrix, constructing an importance degree matrix of three second-level indexes of quality, delivery and service to the first-level index in the same way:

TABLE 3 two-level index decision matrix

Supplier evaluation U	Quality Q	Delivery F	Service T
				Quality Q	1	7	8
Delivery F	1/7	1	3
				Service T	1/8	1/3	1

The judgment matrix in table 3 can be obtained by inviting the supplier to evaluate the participating subject to judge according to experience and calculate the average value, and according to the evaluation rule of the '1-9 scale method', the quality is strong and important compared with delivery, and the quality is between the strong and extremely important compared with service; delivery is somewhat important compared to service.

Similarly, the quality Q can be represented by a decision matrix, and the delivery F and service T are:

and (3) calculating: and according to the constructed 3 x 3 judgment matrix U, obtaining a characteristic root and a characteristic vector which satisfy UW ═ λ maxW, wherein λ max is the maximum characteristic value of the judgment matrix U, W is a normalized characteristic vector corresponding to λ max, and the component W of W is the weight of the corresponding element single ordering, namely index weight. For example, the first-level index calculation method is as follows:

and calculating the characteristic vector of the target layer judgment matrix U, and carrying out normalization processing to obtain the weight vector W of the primary index as (0.777, 0.153, 0.070). The maximum eigenvalue λ max of the determination matrix U is further calculated to be 3.104.

Similarly, weight vectors of 3 first-level indexes Q, F and T are respectively calculated:

mass weight distribution: WQ ═ (0.275, 0.275, 0.150, 0.040, 0.042, 0.075, 0.143); λ max of 7.022

Delivery weight distribution: WF ═ 0.143, 0.286, 0.571; λ max of 3.000

Serving weight distribution WT ═ 0.667, 0.222, 0.111; λ max of 3.000

By sorting the data, the weight values of the indexes of each level to the total target can be obtained, which is shown in table 4.

TABLE 4 Overall weight values and rankings of the elements of the index layer

The weights of the first-level index and the second-level index can be obtained by calculating the weight vector. For the target layer capability evaluation index, the quality weight is 0.777, the delivery weight is 0.153, and the service weight is 0.070. The weights of the secondary indexes to the primary indexes are shown in table 4, wherein the highest weight ratio of the 13 secondary indexes is the qualification rate of the incoming inspection batch and the production offline rate PPM, and the weight is 0.214.

Checking consistency

According to the analytic hierarchy process calculation method, consistency check needs to be carried out on results, and whether the tolerance of the inconsistency degree is in an allowable range is judged. Firstly, calculating a consistency index, wherein at the moment, the matrixes are consistent; the larger the CI, the higher the degree of matrix inconsistency. After the random consistency index RI is inquired according to the matrix level, the consistency ratio can be obtained, and at this time, the degree of inconsistency of the matrix is within the allowable range, at this time, the feature vector of the matrix can be used as the weight vector, table 6 is the consistency check performed on the weight vector, and all the results from the CR result are less than 0.1, so that the performance evaluation model of the supplier has consistency and feasibility.

TABLE 5 average random consistency index

n	1	2	3	4	5	6	7	8	9
										RI	0.00	0.00	0.58	0.90	1.12	1.24	1.32	1.41	1.45

TABLE 6 materials supplier Performance assessment model consistency check

Step S400, calculation rule setting

Three different sets of evaluation rules are set for different indexes:

grading and scoring: and (3) calculating indexes such as production yield and the like by adopting a PPM quality system, namely a defect statistical method of parts per million, and presetting a PPM range and a corresponding score. For example, when the PPM is located within the range of 0-25, the corresponding score is 25 points, when the PPM is located within the range of 26-50, the corresponding score is 24 points, and so on;

counting and scoring: recording the occurrence frequency of the index, multiplying the occurrence frequency by a scoring coefficient to obtain a final value of the index, and if customer complaints are reduced by 5 points for each 1 time, 8D reports that the occurrence frequency is reduced by 2 points for each 1 time, selecting that the occurrence frequency is reduced by 2 points for each 1 time, and the occurrence frequency is reduced by 3 points for each 1 time when batch quality is abnormal, rationalizing and proposing that the occurrence frequency is increased by 5 points for each 1 time;

thirdly, standardized scoring: counting according to the relative value of the total supply quantity or standardizing the index value, and converting the index value into a dimensionless and magnitude-free difference standard value (calculating Z value) to avoid the absolute value difference of partial indexes caused by the difference of the total supply quantity or the prolonging of a time period (for example, calculating annual or semiannual evaluation), and the specific implementation mode is as follows: the index values of different suppliers and different materials of the same material are obtained by standardization treatment of (original value-mean value)/standard deviation.

And S500, outputting the evaluation result and displaying the key data phase.

The invention can integrate a plurality of service system data such as MES, WMS, SRM, ERP and the like and enterprise information crawler data, realize high-frequency data acquisition and data convergence, and greatly reduce the acquisition threshold and the acquisition difficulty of supplier evaluation data sources; the method includes the steps that a supplier evaluation system is built and optimized by referring to the supplier management specifications passed in the industry, and suppliers are evaluated from multiple dimensions, so that multiple key evaluation indexes such as product quality, response timeliness and arrival accuracy are covered; a dynamic weight adjustment model taking an AHP analytic hierarchy process as a core is used to meet the evaluation requirements of suppliers under different backgrounds; the various indexes are automatically graded, scored and standardized, so that the real-time evaluation of the whole indexes and the subdivided indexes of the suppliers and the real-time output of evaluation results are realized, and the evaluation supervision and management of the supplier behaviors are carried out on multiple data sources, multiple indexes, multiple weights and multiple rules.

Finally, it should be understood that the embodiments of the application disclosed herein are illustrative of the principles of the embodiments of the present application. Other modified embodiments are also within the scope of the present application. Accordingly, the disclosed embodiments are presented by way of example only, and not limitation. Those skilled in the art may implement the present application in alternative configurations according to the embodiments of the present application. Thus, embodiments of the present application are not limited to those precisely described in the application.

Claims

1. A supplier intelligent evaluation and grading system based on multi-dimensional material data is characterized by comprising a basic information maintenance module, an evaluation calculation kernel module, an evaluation result output module, a key data item display module and a user and authority module;

2. The multidimensional material data based supplier intelligent evaluation and grading system of claim 1, wherein the dynamic weight model is constructed by the following steps: establishing a hierarchical structure model according to an evaluation system of an evaluation calculation kernel, and establishing a judgment matrix of the importance degree of each index corresponding to a superior index; calculating a feature vector and carrying out normalization processing to obtain a weight vector; and carrying out consistency check on the weight calculation result.

3. The multi-dimensional material data-based supplier intelligent evaluation and grading system according to claim 2, wherein the calculation rule is set by the method comprising the following steps:

4. The multi-dimensional material data-based supplier intelligent evaluation and grading system according to claim 3, wherein the evaluation index implements three sets of evaluation rules:

5. The multi-dimensional material data-based intelligent evaluation and grading system for suppliers according to claim 1, wherein the evaluation grade catalog at least comprises data visualization charts of all evaluation results, key index values, evaluation grade ratios of suppliers and trends of suppliers, and provides self-service analysis functions of screening, sorting and exporting; the supplier evaluation detail billboard displays basic information, subdivision index evaluation results, evaluation calculation processes, traceability data, purchase records, material information, historical evaluation, evaluation scores, evaluation grades and similar suppliers of a certain company.

6. The multi-dimensional material data-based supplier intelligent evaluation and grading system according to claim 1, wherein the key data item display module display mode and display content specifically are: adding key data items of the number of suppliers, the material number, the average score and the evaluation completion times on a system home page for displaying, and simultaneously displaying the average score and the variation condition of the ring ratio of the number of unqualified suppliers in the month; the number of the suppliers is the total number of the suppliers participating in the evaluation of the suppliers in the current month; the number of the materials is the total number of the materials involved in the evaluation of the supplier in the current month; the average score is the arithmetic mean of the final evaluation scores of all suppliers participating in the evaluation of the suppliers in the month; the evaluation completion times are the accumulated times for completing the evaluation of the supplier in all time, and comprise monthly evaluation completion times, semiannual evaluation completion times and annual evaluation completion times.